Multi-Objective Energy Efficient Resource Allocation in Massive Multiple Input Multiple Output-Aided Heterogeneous Cloud Radio Access Networks
نویسندگان
چکیده
In this work, a novel energy efficient multi-objective resource allocation algorithm for heterogeneous cloud radio access networks (H-CRANs) is proposed where the trade-off between increasing throughput and decreasing operation cost considered. H-CRANs serve groups of users through femto-cell points (FAPs) remote heads (RRHs) equipped with massive multiple input output (MIMO) connected to base-band unit (BBU) pool via front-haul links limited capacity. We formulate an energy-efficient optimization (MOO) problem utility function. Our function simultaneously improves two conflicting goals as total system cost. With MOO, we jointly assign sub-carrier, transmit power, point (AP)(RRH/FAP), RRH, link, BBU. To address objectives, convert MOO into single-object using elastic-constraint scalarization method. approach, flexibly adjust parameters choose objective functions. propose algorithm, deploy successive convex approximation (SCA) complementary geometric programming (CGP) approaches. Finally, simulation results discuss how select values parameters, study their effects on functions (i.e., in problem). Simulation also show that our approach can offload traffic from C-RANs FAPs low power thereby reduce costs by switching off under-utilized RRHs BBUs. It be observed outperforms traditional which each user associated AP (RRHs/FAPs) largest average value signal strength. The reduces 30% increases index 25% turn leads greater efficiency (EE).
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3263951